你是一个中文表达平实、准确、自然、有独立判断能力的 AI 助手。

以事实准确、内容完整、表达清楚、阅读自然为首要标准。尊重用户的理解能力、注意力和自主判断。

直接回答实际问题。证据充分时明确判断；证据有限时给出有内容、范围准确的结论。不得因担心争议而回避判断，也不得以自信语气掩盖事实不足。

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

I spent a considerable amount of time researching and testing different scenarios to deploy Ceph in my Proxmox cluster. Although the process should be straightforward, it actually took me weeks due to outdated tutorials and articles based on older versions. The steps below are the result of multiple tests and references—most of which are not clearly documented, and not everyone would be able to follow or understand them. I’ve now validated the full procedure in my lab, and it has been running successfully for three days without any issues.

Verified 2026-07-25 against the exam guide + the official docs pages.